使用pivot_longer将数据形状从宽改为长

我想使用pivot_对该数据帧进行更长时间的重新塑造-

# A tibble: 5 x 7
  PizzaNumber Topping_1 Category_1  Topping_2 Category_2  Topping_3 Category_3 
        <int> <fct>     <fct>       <fct>     <fct>       <fct>     <fct>      
1           1 cheese    vegetarian  ham       carnivorous tomato    vegetarian 
2           2 spinach   vegetarian  tomato    vegetarian  NA        NA         
3           3 pineapple vegetarian  cheese    vegetarian  ham       carnivorous
4           4 cheese    vegetarian  tomato    vegetarian  NA        NA         
5           5 beef      carnivorous NA        NA          NA        NA  

---

我们可以结合使用

dplyr

、

tidyr

和

purrr

：

library(dplyr) # 1.0.0
library(tidyr) # 1.1.0
library(purrr) # 0.3.4

widedata %>% group_by(PizzaNumber) %>% nest() %>%
        mutate(
                Topping_Category = map(data, function(data) {
                        as.list(paste(data[, c(1, 3, 5)], data[, c(2, 4, 6)], sep = "_"))
                })) %>% select(-data) %>%
        unnest_longer(Topping_Category, indices_include = FALSE) %>%
        ungroup() %>%
        separate(Topping_Category, c("Topping", "Category"), sep = "_") %>%
        mutate(Topping = na_if(Topping, "NA"), Category = na_if(Category, "NA")) %>%
        filter(Topping != is.na(Topping))

---

使用

pivot\u更长的时间

您可以-

tidyr::pivot_longer(df, cols = -PizzaNumber, 
                    names_to = '.value', 
                    names_pattern = '(\\w+)_\\d+', 
                    values_drop_na = TRUE)

#  PizzaNumber Topping   Category   
#   <chr>       <chr>     <chr>      
# 1 1           cheese    vegetarian 
# 2 1           ham       carnivorous
# 3 1           tomato    vegetarian 
# 4 2           spinach   vegetarian 
# 5 2           tomato    vegetarian 
# 6 3           pineapple vegetarian 
# 7 3           cheese    vegetarian 
# 8 3           ham       carnivorous
# 9 4           cheese    vegetarian 
#10 4           tomato    vegetarian 
#11 5           beef      carnivorous

tidyr:：pivot_更长（df，cols=-PizzaNumber，
名称_to='.value'，
名称\u模式='（\\w+）\ud+，
值_drop_na=TRUE）
#比萨饼类
#                     
#1奶酪素食者
#2 1火腿肉食性
#3 1番茄素食者
#4.2菠菜素食者
#5.2番茄素食者
#菠萝素食者
#7.3奶酪素食者
#8.3火腿肉食性
#9.4奶酪素食者
#10 4番茄素食者
#11.5肉食牛肉

library(dplyr) # 1.0.0
library(tidyr) # 1.1.0
library(purrr) # 0.3.4

widedata %>% group_by(PizzaNumber) %>% nest() %>%
        mutate(
                Topping_Category = map(data, function(data) {
                        as.list(paste(data[, c(1, 3, 5)], data[, c(2, 4, 6)], sep = "_"))
                })) %>% select(-data) %>%
        unnest_longer(Topping_Category, indices_include = FALSE) %>%
        ungroup() %>%
        separate(Topping_Category, c("Topping", "Category"), sep = "_") %>%
        mutate(Topping = na_if(Topping, "NA"), Category = na_if(Category, "NA")) %>%
        filter(Topping != is.na(Topping))

# A tibble: 11 x 3
   PizzaNumber Topping   Category   
         <dbl> <chr>     <chr>      
 1           1 cheese    vegetarian 
 2           1 ham       carnivorous
 3           1 tomato    vegetarian 
 4           2 spinach   vegetarian 
 5           2 tomato    vegetarian 
 6           3 pineapple vegetarian 
 7           3 cheese    vegetarian 
 8           3 ham       carnivorous
 9           4 cheese    vegetarian 
10           4 tomato    vegetarian 
11           5 beef      carnivorous

structure(list(PizzaNumber = c("1", "2", "3", "4", "5"), Topping_1 = c("cheese", 
"spinach", "pineapple", "cheese", "beef"), Category_1 = c("vegetarian", 
"vegetarian", "vegetarian", "vegetarian", "carnivorous"), Topping_2 = c("ham", 
"tomato", "cheese", "tomato", NA), Category_2 = c("carnivorous", 
"vegetarian", "vegetarian", "vegetarian", NA), Topping_3 = c("tomato", 
NA, "ham", NA, NA), Category_3 = c("vegetarian", NA, "carnivorous", 
NA, NA)), row.names = c(NA, -5L), class = c("tbl_df", "tbl", 
"data.frame"))

tidyr::pivot_longer(df, cols = -PizzaNumber, 
                    names_to = '.value', 
                    names_pattern = '(\\w+)_\\d+', 
                    values_drop_na = TRUE)

#  PizzaNumber Topping   Category   
#   <chr>       <chr>     <chr>      
# 1 1           cheese    vegetarian 
# 2 1           ham       carnivorous
# 3 1           tomato    vegetarian 
# 4 2           spinach   vegetarian 
# 5 2           tomato    vegetarian 
# 6 3           pineapple vegetarian 
# 7 3           cheese    vegetarian 
# 8 3           ham       carnivorous
# 9 4           cheese    vegetarian 
#10 4           tomato    vegetarian 
#11 5           beef      carnivorous